Overview

Dataset statistics

Number of variables21
Number of observations21597
Missing cells6281
Missing cells (%)1.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.5 MiB
Average record size in memory168.0 B

Variable types

NUM18
CAT2
BOOL1

Reproduction

Analysis started2020-08-06 12:43:12.810734
Analysis finished2020-08-06 12:43:56.611419
Duration43.8 seconds
Versionpandas-profiling v2.8.0
Command linepandas_profiling --config_file config.yaml [YOUR_FILE.csv]
Download configurationconfig.yaml

Warnings

date has a high cardinality: 372 distinct values High cardinality
sqft_basement has a high cardinality: 304 distinct values High cardinality
waterfront has 2376 (11.0%) missing values Missing
yr_renovated has 3842 (17.8%) missing values Missing
view has 19422 (89.9%) zeros Zeros
yr_renovated has 17011 (78.8%) zeros Zeros

Variables

id
Real number (ℝ≥0)

Distinct count21420
Unique (%)99.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4580474287.770987
Minimum1000102
Maximum9900000190
Zeros0
Zeros (%)0.0%
Memory size168.7 KiB
2020-08-06T07:43:56.700216image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum1000102
5-th percentile512740390
Q12123049175
median3904930410
Q37308900490
95-th percentile9297300412
Maximum9900000190
Range9899000088
Interquartile range (IQR)5185851315

Descriptive statistics

Standard deviation2876735716
Coefficient of variation (CV)0.6280431971
Kurtosis-1.260749894
Mean4580474288
Median Absolute Deviation (MAD)2402530270
Skewness0.243225522
Sum9.892450319e+13
Variance8.275608378e+18
2020-08-06T07:43:56.784987image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
7950006203< 0.1%
 
18250690312< 0.1%
 
20192002202< 0.1%
 
71293045402< 0.1%
 
17815004352< 0.1%
 
39693000302< 0.1%
 
25608012222< 0.1%
 
38838000112< 0.1%
 
22289002702< 0.1%
 
2513001102< 0.1%
 
Other values (21410)2157699.9%
 
ValueCountFrequency (%) 
10001022< 0.1%
 
12000191< 0.1%
 
12000211< 0.1%
 
28000311< 0.1%
 
36000571< 0.1%
 
ValueCountFrequency (%) 
99000001901< 0.1%
 
98950000401< 0.1%
 
98423005401< 0.1%
 
98423004851< 0.1%
 
98423000951< 0.1%
 

date
Categorical

HIGH CARDINALITY

Distinct count372
Unique (%)1.7%
Missing0
Missing (%)0.0%
Memory size168.7 KiB
6/23/2014
 
142
6/25/2014
 
131
6/26/2014
 
131
7/8/2014
 
127
4/27/2015
 
126
Other values (367)
20940
ValueCountFrequency (%) 
6/23/20141420.7%
 
6/25/20141310.6%
 
6/26/20141310.6%
 
7/8/20141270.6%
 
4/27/20151260.6%
 
3/25/20151230.6%
 
4/22/20151210.6%
 
7/9/20141210.6%
 
4/28/20151210.6%
 
4/14/20151210.6%
 
Other values (362)2033394.1%
 
2020-08-06T07:43:56.973486image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Length

Max length10
Median length9
Mean length8.924433949
Min length8

price
Real number (ℝ≥0)

Distinct count3622
Unique (%)16.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean540296.5735055795
Minimum78000.0
Maximum7700000.0
Zeros0
Zeros (%)0.0%
Memory size168.7 KiB
2020-08-06T07:43:57.067240image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum78000
5-th percentile210000
Q1322000
median450000
Q3645000
95-th percentile1160000
Maximum7700000
Range7622000
Interquartile range (IQR)323000

Descriptive statistics

Standard deviation367368.1401
Coefficient of variation (CV)0.6799379417
Kurtosis34.54135858
Mean540296.5735
Median Absolute Deviation (MAD)150000
Skewness4.023364652
Sum1.16687851e+10
Variance1.349593504e+11
2020-08-06T07:43:57.164939image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
3500001720.8%
 
4500001720.8%
 
5500001590.7%
 
5000001520.7%
 
4250001500.7%
 
3250001480.7%
 
4000001450.7%
 
3750001380.6%
 
3000001330.6%
 
5250001310.6%
 
Other values (3612)2009793.1%
 
ValueCountFrequency (%) 
780001< 0.1%
 
800001< 0.1%
 
810001< 0.1%
 
820001< 0.1%
 
825001< 0.1%
 
ValueCountFrequency (%) 
77000001< 0.1%
 
70600001< 0.1%
 
68900001< 0.1%
 
55700001< 0.1%
 
53500001< 0.1%
 

bedrooms
Real number (ℝ≥0)

Distinct count12
Unique (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3731999814789093
Minimum1
Maximum33
Zeros0
Zeros (%)0.0%
Memory size168.7 KiB
2020-08-06T07:43:57.261681image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median3
Q34
95-th percentile5
Maximum33
Range32
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.9262988945
Coefficient of variation (CV)0.2746053894
Kurtosis49.82183475
Mean3.373199981
Median Absolute Deviation (MAD)1
Skewness2.023641235
Sum72851
Variance0.858029642
2020-08-06T07:43:57.349481image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
3982445.5%
 
4688231.9%
 
2276012.8%
 
516017.4%
 
62721.3%
 
11960.9%
 
7380.2%
 
8130.1%
 
96< 0.1%
 
103< 0.1%
 
Other values (2)2< 0.1%
 
ValueCountFrequency (%) 
11960.9%
 
2276012.8%
 
3982445.5%
 
4688231.9%
 
516017.4%
 
ValueCountFrequency (%) 
331< 0.1%
 
111< 0.1%
 
103< 0.1%
 
96< 0.1%
 
8130.1%
 

bathrooms
Real number (ℝ≥0)

Distinct count29
Unique (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1158262721674306
Minimum0.5
Maximum8.0
Zeros0
Zeros (%)0.0%
Memory size168.7 KiB
2020-08-06T07:43:57.437245image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile1
Q11.75
median2.25
Q32.5
95-th percentile3.5
Maximum8
Range7.5
Interquartile range (IQR)0.75

Descriptive statistics

Standard deviation0.7689842967
Coefficient of variation (CV)0.3634439683
Kurtosis1.279315294
Mean2.115826272
Median Absolute Deviation (MAD)0.5
Skewness0.5197092816
Sum45695.5
Variance0.5913368485
2020-08-06T07:43:57.518028image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
2.5537724.9%
 
1385117.8%
 
1.75304814.1%
 
2.2520479.5%
 
219308.9%
 
1.514456.7%
 
2.7511855.5%
 
37533.5%
 
3.57313.4%
 
3.255892.7%
 
Other values (19)6413.0%
 
ValueCountFrequency (%) 
0.54< 0.1%
 
0.75710.3%
 
1385117.8%
 
1.259< 0.1%
 
1.514456.7%
 
ValueCountFrequency (%) 
82< 0.1%
 
7.751< 0.1%
 
7.51< 0.1%
 
6.752< 0.1%
 
6.52< 0.1%
 

sqft_living
Real number (ℝ≥0)

Distinct count1034
Unique (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2080.3218502569803
Minimum370
Maximum13540
Zeros0
Zeros (%)0.0%
Memory size168.7 KiB
2020-08-06T07:43:57.605794image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum370
5-th percentile940
Q11430
median1910
Q32550
95-th percentile3760
Maximum13540
Range13170
Interquartile range (IQR)1120

Descriptive statistics

Standard deviation918.1061251
Coefficient of variation (CV)0.4413288862
Kurtosis5.252101951
Mean2080.32185
Median Absolute Deviation (MAD)540
Skewness1.473215455
Sum44928711
Variance842918.8569
2020-08-06T07:43:57.691567image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
13001380.6%
 
14001350.6%
 
14401330.6%
 
16601290.6%
 
10101290.6%
 
18001290.6%
 
18201280.6%
 
14801250.6%
 
17201250.6%
 
15401240.6%
 
Other values (1024)2030294.0%
 
ValueCountFrequency (%) 
3701< 0.1%
 
3801< 0.1%
 
3901< 0.1%
 
4101< 0.1%
 
4202< 0.1%
 
ValueCountFrequency (%) 
135401< 0.1%
 
120501< 0.1%
 
100401< 0.1%
 
98901< 0.1%
 
96401< 0.1%
 

sqft_lot
Real number (ℝ≥0)

Distinct count9776
Unique (%)45.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15099.408760475992
Minimum520
Maximum1651359
Zeros0
Zeros (%)0.0%
Memory size168.7 KiB
2020-08-06T07:43:57.782322image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum520
5-th percentile1800.8
Q15040
median7618
Q310685
95-th percentile43307.2
Maximum1651359
Range1650839
Interquartile range (IQR)5645

Descriptive statistics

Standard deviation41412.63688
Coefficient of variation (CV)2.742666122
Kurtosis285.4958119
Mean15099.40876
Median Absolute Deviation (MAD)2618
Skewness13.07260357
Sum326101931
Variance1715006493
2020-08-06T07:43:57.875080image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
50003581.7%
 
60002901.3%
 
40002511.2%
 
72002201.0%
 
75001190.6%
 
48001190.6%
 
45001140.5%
 
84001110.5%
 
96001090.5%
 
36001030.5%
 
Other values (9766)1980391.7%
 
ValueCountFrequency (%) 
5201< 0.1%
 
5721< 0.1%
 
6001< 0.1%
 
6091< 0.1%
 
6351< 0.1%
 
ValueCountFrequency (%) 
16513591< 0.1%
 
11647941< 0.1%
 
10742181< 0.1%
 
10240681< 0.1%
 
9829981< 0.1%
 

floors
Real number (ℝ≥0)

Distinct count6
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4940964022780943
Minimum1.0
Maximum3.5
Zeros0
Zeros (%)0.0%
Memory size168.7 KiB
2020-08-06T07:43:57.968826image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1.5
Q32
95-th percentile2
Maximum3.5
Range2.5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.539682791
Coefficient of variation (CV)0.3612101536
Kurtosis-0.4910657592
Mean1.494096402
Median Absolute Deviation (MAD)0.5
Skewness0.6144969756
Sum32268
Variance0.2912575149
2020-08-06T07:43:58.043625image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
11067349.4%
 
2823538.1%
 
1.519108.8%
 
36112.8%
 
2.51610.7%
 
3.57< 0.1%
 
ValueCountFrequency (%) 
11067349.4%
 
1.519108.8%
 
2823538.1%
 
2.51610.7%
 
36112.8%
 
ValueCountFrequency (%) 
3.57< 0.1%
 
36112.8%
 
2.51610.7%
 
2823538.1%
 
1.519108.8%
 

waterfront
Boolean

MISSING

Distinct count2
Unique (%)< 0.1%
Missing2376
Missing (%)11.0%
Memory size168.7 KiB
0
19075
1
 
146
(Missing)
 
2376
ValueCountFrequency (%) 
01907588.3%
 
11460.7%
 
(Missing)237611.0%
 

view
Real number (ℝ≥0)

ZEROS

Distinct count5
Unique (%)< 0.1%
Missing63
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean0.23386272870808952
Minimum0.0
Maximum4.0
Zeros19422
Zeros (%)89.9%
Memory size168.7 KiB
2020-08-06T07:43:58.125407image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum4
Range4
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.7656862012
Coefficient of variation (CV)3.274083927
Kurtosis10.91971254
Mean0.2338627287
Median Absolute Deviation (MAD)0
Skewness3.399525635
Sum5036
Variance0.5862753587
2020-08-06T07:43:58.212141image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
01942289.9%
 
29574.4%
 
35082.4%
 
13301.5%
 
43171.5%
 
(Missing)630.3%
 
ValueCountFrequency (%) 
01942289.9%
 
13301.5%
 
29574.4%
 
35082.4%
 
43171.5%
 
ValueCountFrequency (%) 
43171.5%
 
35082.4%
 
29574.4%
 
13301.5%
 
01942289.9%
 

condition
Real number (ℝ≥0)

Distinct count5
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4098254387183404
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Memory size168.7 KiB
2020-08-06T07:43:58.302932image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q13
median3
Q34
95-th percentile5
Maximum5
Range4
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.6505456357
Coefficient of variation (CV)0.1907856127
Kurtosis0.5192374924
Mean3.409825439
Median Absolute Deviation (MAD)0
Skewness1.036037425
Sum73642
Variance0.4232096241
2020-08-06T07:43:58.385710image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
31402064.9%
 
4567726.3%
 
517017.9%
 
21700.8%
 
1290.1%
 
ValueCountFrequency (%) 
1290.1%
 
21700.8%
 
31402064.9%
 
4567726.3%
 
517017.9%
 
ValueCountFrequency (%) 
517017.9%
 
4567726.3%
 
31402064.9%
 
21700.8%
 
1290.1%
 

grade
Real number (ℝ≥0)

Distinct count11
Unique (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.657915451220076
Minimum3
Maximum13
Zeros0
Zeros (%)0.0%
Memory size168.7 KiB
2020-08-06T07:43:58.478462image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile6
Q17
median7
Q38
95-th percentile10
Maximum13
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.173199664
Coefficient of variation (CV)0.1532009163
Kurtosis1.135148022
Mean7.657915451
Median Absolute Deviation (MAD)1
Skewness0.7882366364
Sum165388
Variance1.376397451
2020-08-06T07:43:58.557218image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
7897441.6%
 
8606528.1%
 
9261512.1%
 
620389.4%
 
1011345.3%
 
113991.8%
 
52421.1%
 
12890.4%
 
4270.1%
 
13130.1%
 
ValueCountFrequency (%) 
31< 0.1%
 
4270.1%
 
52421.1%
 
620389.4%
 
7897441.6%
 
ValueCountFrequency (%) 
13130.1%
 
12890.4%
 
113991.8%
 
1011345.3%
 
9261512.1%
 

sqft_above
Real number (ℝ≥0)

Distinct count942
Unique (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1788.5968421540028
Minimum370
Maximum9410
Zeros0
Zeros (%)0.0%
Memory size168.7 KiB
2020-08-06T07:43:58.640005image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum370
5-th percentile850
Q11190
median1560
Q32210
95-th percentile3400
Maximum9410
Range9040
Interquartile range (IQR)1020

Descriptive statistics

Standard deviation827.7597612
Coefficient of variation (CV)0.4627984024
Kurtosis3.405519761
Mean1788.596842
Median Absolute Deviation (MAD)450
Skewness1.447434235
Sum38628326
Variance685186.2222
2020-08-06T07:43:58.717797image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
13002121.0%
 
10102101.0%
 
12002061.0%
 
12201920.9%
 
11401840.9%
 
14001800.8%
 
10601780.8%
 
11801770.8%
 
13401760.8%
 
12501740.8%
 
Other values (932)1970891.3%
 
ValueCountFrequency (%) 
3701< 0.1%
 
3801< 0.1%
 
3901< 0.1%
 
4101< 0.1%
 
4202< 0.1%
 
ValueCountFrequency (%) 
94101< 0.1%
 
88601< 0.1%
 
85701< 0.1%
 
80201< 0.1%
 
78801< 0.1%
 

sqft_basement
Categorical

HIGH CARDINALITY

Distinct count304
Unique (%)1.4%
Missing0
Missing (%)0.0%
Memory size168.7 KiB
0.0
12826
?
 
454
600.0
 
217
500.0
 
209
700.0
 
208
Other values (299)
7683
ValueCountFrequency (%) 
0.01282659.4%
 
?4542.1%
 
600.02171.0%
 
500.02091.0%
 
700.02081.0%
 
800.02010.9%
 
400.01840.9%
 
1000.01480.7%
 
300.01420.7%
 
900.01420.7%
 
Other values (294)686631.8%
 
2020-08-06T07:43:58.898307image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Length

Max length6
Median length3
Mean length3.816039265
Min length1

yr_built
Real number (ℝ≥0)

Distinct count116
Unique (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1970.9996758809093
Minimum1900
Maximum2015
Zeros0
Zeros (%)0.0%
Memory size168.7 KiB
2020-08-06T07:43:58.990101image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum1900
5-th percentile1915
Q11951
median1975
Q31997
95-th percentile2011
Maximum2015
Range115
Interquartile range (IQR)46

Descriptive statistics

Standard deviation29.37523413
Coefficient of variation (CV)0.01490372347
Kurtosis-0.6576944258
Mean1970.999676
Median Absolute Deviation (MAD)23
Skewness-0.4694499765
Sum42567680
Variance862.9043803
2020-08-06T07:43:59.073870image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
20145592.6%
 
20064532.1%
 
20054502.1%
 
20044332.0%
 
20034201.9%
 
20074171.9%
 
19774171.9%
 
19783871.8%
 
19683811.8%
 
20083671.7%
 
Other values (106)1731380.2%
 
ValueCountFrequency (%) 
1900870.4%
 
1901290.1%
 
1902270.1%
 
1903460.2%
 
1904450.2%
 
ValueCountFrequency (%) 
2015380.2%
 
20145592.6%
 
20132010.9%
 
20121700.8%
 
20111300.6%
 

yr_renovated
Real number (ℝ≥0)

MISSING
ZEROS

Distinct count70
Unique (%)0.4%
Missing3842
Missing (%)17.8%
Infinite0
Infinite (%)0.0%
Mean83.6367783722895
Minimum0.0
Maximum2015.0
Zeros17011
Zeros (%)78.8%
Memory size168.7 KiB
2020-08-06T07:43:59.165626image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum2015
Range2015
Interquartile range (IQR)0

Descriptive statistics

Standard deviation399.9464139
Coefficient of variation (CV)4.781944279
Kurtosis18.91954345
Mean83.63677837
Median Absolute Deviation (MAD)0
Skewness4.573385242
Sum1484971
Variance159957.134
2020-08-06T07:43:59.257381image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
01701178.8%
 
2014730.3%
 
2003310.1%
 
2013310.1%
 
2007300.1%
 
2005290.1%
 
2000290.1%
 
1990220.1%
 
2004220.1%
 
2009210.1%
 
Other values (60)4562.1%
 
(Missing)384217.8%
 
ValueCountFrequency (%) 
01701178.8%
 
19341< 0.1%
 
19402< 0.1%
 
19441< 0.1%
 
19453< 0.1%
 
ValueCountFrequency (%) 
2015140.1%
 
2014730.3%
 
2013310.1%
 
20128< 0.1%
 
20119< 0.1%
 

zipcode
Real number (ℝ≥0)

Distinct count70
Unique (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98077.95184516368
Minimum98001
Maximum98199
Zeros0
Zeros (%)0.0%
Memory size168.7 KiB
2020-08-06T07:43:59.353129image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum98001
5-th percentile98004
Q198033
median98065
Q398118
95-th percentile98177
Maximum98199
Range198
Interquartile range (IQR)85

Descriptive statistics

Standard deviation53.51307235
Coefficient of variation (CV)0.0005456177596
Kurtosis-0.8540048606
Mean98077.95185
Median Absolute Deviation (MAD)42
Skewness0.4053221913
Sum2118189526
Variance2863.648913
2020-08-06T07:43:59.440888image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
981036022.8%
 
980385892.7%
 
981155832.7%
 
980525742.7%
 
981175532.6%
 
980425472.5%
 
980345452.5%
 
981185072.3%
 
980234992.3%
 
980064982.3%
 
Other values (60)1610074.5%
 
ValueCountFrequency (%) 
980013611.7%
 
980021990.9%
 
980032801.3%
 
980043171.5%
 
980051680.8%
 
ValueCountFrequency (%) 
981993171.5%
 
981982801.3%
 
981881360.6%
 
981782621.2%
 
981772551.2%
 

lat
Real number (ℝ≥0)

Distinct count5033
Unique (%)23.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.56009299439737
Minimum47.1559
Maximum47.7776
Zeros0
Zeros (%)0.0%
Memory size168.7 KiB
2020-08-06T07:43:59.539593image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum47.1559
5-th percentile47.3103
Q147.4711
median47.5718
Q347.678
95-th percentile47.7497
Maximum47.7776
Range0.6217
Interquartile range (IQR)0.2069

Descriptive statistics

Standard deviation0.1385517682
Coefficient of variation (CV)0.002913193803
Kurtosis-0.6757902106
Mean47.56009299
Median Absolute Deviation (MAD)0.1049
Skewness-0.48552159
Sum1027155.328
Variance0.01919659246
2020-08-06T07:43:59.639328image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
47.6624170.1%
 
47.5491170.1%
 
47.5322170.1%
 
47.6846170.1%
 
47.6711160.1%
 
47.6886160.1%
 
47.6955160.1%
 
47.6647150.1%
 
47.6904150.1%
 
47.686150.1%
 
Other values (5023)2143699.3%
 
ValueCountFrequency (%) 
47.15591< 0.1%
 
47.15931< 0.1%
 
47.16221< 0.1%
 
47.16471< 0.1%
 
47.17641< 0.1%
 
ValueCountFrequency (%) 
47.77763< 0.1%
 
47.77753< 0.1%
 
47.77741< 0.1%
 
47.77723< 0.1%
 
47.77712< 0.1%
 

long
Real number (ℝ)

Distinct count751
Unique (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-122.2139824975691
Minimum-122.51899999999999
Maximum-121.315
Zeros0
Zeros (%)0.0%
Memory size168.7 KiB
2020-08-06T07:43:59.735104image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum-122.519
5-th percentile-122.387
Q1-122.328
median-122.231
Q3-122.125
95-th percentile-121.9798
Maximum-121.315
Range1.204
Interquartile range (IQR)0.203

Descriptive statistics

Standard deviation0.1407235288
Coefficient of variation (CV)-0.001151451953
Kurtosis1.052120317
Mean-122.2139825
Median Absolute Deviation (MAD)0.101
Skewness0.8848883395
Sum-2639455.38
Variance0.01980311157
2020-08-06T07:43:59.817850image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-122.291150.5%
 
-122.31110.5%
 
-122.3621040.5%
 
-122.2911000.5%
 
-122.372990.5%
 
-122.363990.5%
 
-122.288980.5%
 
-122.357960.4%
 
-122.284950.4%
 
-122.172940.4%
 
Other values (741)2058695.3%
 
ValueCountFrequency (%) 
-122.5191< 0.1%
 
-122.5151< 0.1%
 
-122.5141< 0.1%
 
-122.5121< 0.1%
 
-122.5112< 0.1%
 
ValueCountFrequency (%) 
-121.3152< 0.1%
 
-121.3161< 0.1%
 
-121.3191< 0.1%
 
-121.3211< 0.1%
 
-121.3251< 0.1%
 

sqft_living15
Real number (ℝ≥0)

Distinct count777
Unique (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1986.6203176367087
Minimum399
Maximum6210
Zeros0
Zeros (%)0.0%
Memory size168.7 KiB
2020-08-06T07:43:59.909603image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum399
5-th percentile1140
Q11490
median1840
Q32360
95-th percentile3300
Maximum6210
Range5811
Interquartile range (IQR)870

Descriptive statistics

Standard deviation685.2304719
Coefficient of variation (CV)0.3449227141
Kurtosis1.591732789
Mean1986.620318
Median Absolute Deviation (MAD)410
Skewness1.106875397
Sum42905039
Variance469540.7996
2020-08-06T07:43:59.996400image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
15401970.9%
 
14401950.9%
 
15601920.9%
 
15001800.8%
 
14601690.8%
 
15801670.8%
 
16101660.8%
 
18001660.8%
 
17201660.8%
 
16201640.8%
 
Other values (767)1983591.8%
 
ValueCountFrequency (%) 
3991< 0.1%
 
4602< 0.1%
 
6202< 0.1%
 
6701< 0.1%
 
6902< 0.1%
 
ValueCountFrequency (%) 
62101< 0.1%
 
61101< 0.1%
 
57906< 0.1%
 
56101< 0.1%
 
56001< 0.1%
 

sqft_lot15
Real number (ℝ≥0)

Distinct count8682
Unique (%)40.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12758.283511598833
Minimum651
Maximum871200
Zeros0
Zeros (%)0.0%
Memory size168.7 KiB
2020-08-06T07:44:00.102123image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum651
5-th percentile2002.4
Q15100
median7620
Q310083
95-th percentile37045.2
Maximum871200
Range870549
Interquartile range (IQR)4983

Descriptive statistics

Standard deviation27274.44195
Coefficient of variation (CV)2.137783027
Kurtosis151.3956625
Mean12758.28351
Median Absolute Deviation (MAD)2505
Skewness9.524361965
Sum275540649
Variance743895183.7
2020-08-06T07:44:00.192879image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
50004272.0%
 
40003561.6%
 
60002881.3%
 
72002101.0%
 
48001450.7%
 
75001420.7%
 
84001160.5%
 
45001110.5%
 
36001110.5%
 
51001090.5%
 
Other values (8672)1958290.7%
 
ValueCountFrequency (%) 
6511< 0.1%
 
6591< 0.1%
 
6601< 0.1%
 
7482< 0.1%
 
7504< 0.1%
 
ValueCountFrequency (%) 
8712001< 0.1%
 
8581321< 0.1%
 
5606171< 0.1%
 
4382131< 0.1%
 
4347281< 0.1%
 

Interactions

2020-08-06T07:43:17.385145image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-06T07:43:17.506851image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-06T07:43:17.618558image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-06T07:43:17.722278image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-06T07:43:17.828990image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-06T07:43:17.937701image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-06T07:43:18.066324image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-06T07:43:18.184011image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-06T07:43:18.303451image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-06T07:43:18.427678image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-06T07:43:18.526439image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-06T07:43:18.623188image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-06T07:43:18.717134image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-06T07:43:18.826467image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-06T07:43:18.937498image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-06T07:43:19.045187image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-06T07:43:19.155913image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-06T07:43:19.258642image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-06T07:43:19.359372image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
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2020-08-06T07:43:19.701458image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-06T07:43:19.819135image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-06T07:43:19.940784image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-06T07:43:20.061492image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-06T07:43:20.178181image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
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2020-08-06T07:43:20.427510image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
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2020-08-06T07:43:20.659862image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-06T07:43:20.767607image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-06T07:43:20.906203image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-06T07:43:21.035889image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-06T07:43:21.141605image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-06T07:43:21.246328image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-06T07:43:21.353046image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-06T07:43:21.459754image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-06T07:43:21.559483image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-06T07:43:21.663219image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-06T07:43:21.762947image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-06T07:43:21.881625image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-06T07:43:21.999280image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-06T07:43:22.109003image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
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2020-08-06T07:43:22.562804image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-06T07:43:22.668527image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-06T07:43:22.786210image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-06T07:43:22.898917image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-06T07:43:23.012618image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-06T07:43:23.119325image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-06T07:43:23.221016image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-06T07:43:23.326734image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-06T07:43:23.437478image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-06T07:43:23.550165image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-06T07:43:23.670815image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-06T07:43:23.785507image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-06T07:43:23.901202image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
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2020-08-06T07:43:24.125633image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-06T07:43:24.228357image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-06T07:43:24.346009image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-06T07:43:24.470706image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-06T07:43:24.579385image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-06T07:43:24.691124image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-06T07:43:24.805814image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-06T07:43:24.943448image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-06T07:43:25.060143image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-06T07:43:25.166856image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-06T07:43:25.270578image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-06T07:43:25.372305image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-06T07:43:25.477026image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-06T07:43:25.581747image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-06T07:43:25.699399image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-06T07:43:25.814136image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-06T07:43:25.939757image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-06T07:43:26.065458image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-06T07:43:26.180157image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-06T07:43:26.294835image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-06T07:43:26.459344image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-06T07:43:26.590292image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-06T07:43:26.705015image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-06T07:43:26.821674image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-06T07:43:26.941354image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-06T07:43:27.050099image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-06T07:43:27.173733image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-06T07:43:27.292443image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-06T07:43:27.406150image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-06T07:43:27.513856image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-06T07:43:27.621563image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-06T07:43:27.724300image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-06T07:43:27.832976image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-06T07:43:27.946706image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-06T07:43:28.053425image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-06T07:43:28.164123image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-06T07:43:28.270834image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-06T07:43:28.374562image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-06T07:43:28.487258image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-06T07:43:28.601954image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-06T07:43:28.704679image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-06T07:43:28.807405image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-06T07:43:28.915121image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-06T07:43:29.032763image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-06T07:43:29.141512image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-06T07:43:29.246227image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-06T07:43:29.349917image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-06T07:43:29.456674image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-06T07:43:29.560388image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-06T07:43:29.661128image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-06T07:43:30.678710image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-06T07:43:30.797422image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-06T07:43:30.916104image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-06T07:43:31.031799image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-06T07:43:31.137484image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-06T07:43:31.233228image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-06T07:43:31.334956image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-06T07:43:31.438679image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-06T07:43:31.535420image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-06T07:43:31.634169image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-06T07:43:31.743861image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-06T07:43:31.858557image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-06T07:43:31.970257image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-06T07:43:32.072983image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-06T07:43:32.175708image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-06T07:43:32.285415image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-06T07:43:32.384151image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
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Correlations

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Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
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Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-08-06T07:44:00.767313image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-08-06T07:44:00.983764image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2020-08-06T07:43:55.696863image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-06T07:43:56.110757image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-06T07:43:56.348124image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-06T07:43:56.469831image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Sample

First rows

iddatepricebedroomsbathroomssqft_livingsqft_lotfloorswaterfrontviewconditiongradesqft_abovesqft_basementyr_builtyr_renovatedzipcodelatlongsqft_living15sqft_lot15
0712930052010/13/2014221,900.031.0118056501.0nan0.03711800.019550.09817847.5112-122.25713405650
1641410019212/9/2014538,000.032.25257072422.00.00.0372170400.019511,991.09812547.721000000000004-122.31916907639
256315004002/25/2015180,000.021.0770100001.00.00.0367700.01933nan9802847.7379-122.2329999999999927208062
3248720087512/9/2014604,000.043.0196050001.00.00.0571050910.019650.09813647.5208-122.3929999999999913605000
419544005102/18/2015510,000.032.0168080801.00.00.03816800.019870.09807447.6168-122.04518007503
572375503105/12/20141,230,000.044.554201019301.00.00.031138901530.020010.09805347.6561-122.0054760101930
613214000606/27/2014257,500.032.25171568192.00.00.0371715?19950.09800347.3097-122.3270000000000122386819
720080002701/15/2015291,850.031.5106097111.00.0nan3710600.019630.09819847.4095-122.31516509711
824146001264/15/2015229,500.031.0178074701.00.00.0371050730.019600.09814647.5123-122.33717808113
937935001603/12/2015323,000.032.5189065602.00.00.03718900.020030.09803847.3684-122.03123907570

Last rows

iddatepricebedroomsbathroomssqft_livingsqft_lotfloorswaterfrontviewconditiongradesqft_abovesqft_basementyr_builtyr_renovatedzipcodelatlongsqft_living15sqft_lot15
2158778521400408/25/2014507,250.032.5227055362.0nan0.03822700.020030.09806547.5389-121.88122705731
2158898342013671/26/2015429,000.032.0149011263.00.00.03814900.020140.09814447.5699-122.28814001230
21589344890021010/14/2014610,685.042.5252060232.00.0nan3925200.020140.09805647.5137-122.16725206023
2159079360004293/26/20151,010,000.043.5351072002.00.00.0392600910.020090.09813647.5537-122.39820506200
2159129978000212/19/2015475,000.032.5131012942.00.00.0381180130.020080.09811647.5773-122.4089999999999913301265
215922630000185/21/2014360,000.032.5153011313.00.00.03815300.020090.09810347.6993-122.34615301509
2159366000601202/23/2015400,000.042.5231058132.00.00.03823100.020140.09814647.5107-122.3620000000000118307200
2159415233001416/23/2014402,101.020.75102013502.00.00.03710200.020090.09814447.5944-122.2989999999999910202007
215952913101001/16/2015400,000.032.5160023882.0nan0.03816000.020040.09802747.5345-122.06914101287
21596152330015710/15/2014325,000.020.75102010762.00.00.03710200.020080.09814447.5941-122.2989999999999910201357